The dqrng package provides several fast random number
generators together with fast functions for generating random numbers
according to a uniform, normal and exponential distribution. These
functions are modeled after the base functions
set.seed, RNGkind, runif,
rnorm, and rexp.
dqRNGkind(kind, normal_kind = "ignored") dqrunif(n, min = 0, max = 1) dqrnorm(n, mean = 0, sd = 1) dqrexp(n, rate = 1) dqset.seed(seed, stream = NULL)
| kind | string specifying the RNG (see details) |
|---|---|
| normal_kind | ignored; included for compatibility with |
| n | number of observations |
| min | lower limit of the uniform distribution |
| max | upper limit of the uniform distribution |
| mean | mean value of the normal distribution |
| sd | standard deviation of the normal distribution |
| rate | rate of the exponential distribution |
| seed | integer scalar to seed the random number generator, or an integer vector of length 2 representing a 64-bit seed. Maybe |
| stream | integer used for selecting the RNG stream; either a scalar or a vector of length 2 |
dqrunif, dqrnorm, and dqrexp return a numeric vector of length n.
Supported RNG kinds:
The default 64 bit variant from the PCG family developed by Melissa O'Neill. See https://www.pcg-random.org/ for more details.
RNGs developed by David Blackman and Sebastiano Vigna. They are used as default RNGs in Erlang and Lua. See https://xoshiro.di.unimi.it/ for more details.
The 64 bit version of the 20 rounds Threefry engine as
provided by sitmo-package
Xoroshiro128+ is the default since it is the fastest generator provided by this package.
The functions dqrnorm and dqrexp use the Ziggurat algorithm as
provided by boost.random.
See generateSeedVectors for rapid generation of integer-vector
seeds that provide 64 bits of entropy. These allow full exploration of
the state space of the 64-bit RNGs provided in this package.
If the provided seed is NULL, a seed is genenrated from R's RNG
without state alteration.
library(dqrng) # Set custom RNG. dqRNGkind("Xoshiro256+") # Use an integer scalar to set a seed. dqset.seed(42) # Use integer scalars to set a seed and the stream. dqset.seed(42, 123) # Use an integer vector to set a seed. dqset.seed(c(31311L, 24123423L)) # Use an integer vector to set a seed and a scalar to select the stream. dqset.seed(c(31311L, 24123423L), 123) # Random sampling from distributions. dqrunif(5, min = 2, max = 10)#> [1] 9.254756 2.169924 3.232695 8.859045 4.940842dqrexp(5, rate = 4)#> [1] 0.003025477 0.213876337 0.008051277 0.399246137 0.066848602dqrnorm(5, mean = 5, sd = 3)#> [1] 1.399092 7.411358 7.145729 2.308440 6.766659